Learning skill preconditions is closely related with symbol learning. Symbolic language formulations explicitly include pre-conditions and post-conditions [1]}.
Thus previous approaches have looked at inferring discrete symbols and predicates from continuous states.
There have been two sets of approaches in this regard.
At one end, previous works have looked into classifying continuous states into pre-defined symbolic forms [2]}, [3]}, [4]}, [5]}. Alternately, other works aim to discover these discrete forms by methods such as clustering [6]}, [7]}, [8]}, [9]}.
Some of the above approaches have also looked into learning discrete representations for object relations. For instance, [5]} uses histogram features extracted from point clouds to classify object relations into discrete categories.
In [9]}, the authors construct a graph of contact points between two objects, which is used to classify object relations using supervised learning.
While [12]} learns discrete spatial relations by geometrically dividing the space around an object into a fixed set of regions.
In contrast to discrete symbolic forms, we focus on learning continuous representations for object relations.
Also, instead of solely focusing on either geometric relations [12]} or contact points [9]}, our approach is grounded in a robot's interaction with pairs of objects.
This allows us to learn continuous relational representations by leveraging spatial geometry, contact information, and object-interaction dynamics all together.
